<FrameworkSwitchCourse {fw} />

# Introduction[[introduction]]

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    chapter={7}
    classNames="absolute z-10 right-0 top-0"
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In [Chapter 3](/course/chapter3), you saw how to fine-tune a model for text classification. In this chapter, we will tackle the following common language tasks that are essential for working with both traditional NLP models and modern LLMs:

- Token classification
- Masked language modeling (like BERT)
- Summarization
- Translation
- Causal language modeling pretraining (like GPT-2)
- Question answering

These fundamental tasks form the foundation of how Large Language Models (LLMs) work and understanding them is crucial for effectively working with today's most advanced language models.

{#if fw === 'pt'}

To do this, you'll need to leverage everything you learned about the `Trainer` API and the 🤗 Accelerate library in [Chapter 3](/course/chapter3), the 🤗 Datasets library in [Chapter 5](/course/chapter5), and the 🤗 Tokenizers library in [Chapter 6](/course/chapter6). We'll also upload our results to the Model Hub, like we did in [Chapter 4](/course/chapter4), so this is really the chapter where everything comes together!

Each section can be read independently and will show you how to train a model with the `Trainer` API or with your own training loop, using 🤗 Accelerate. Feel free to skip either part and focus on the one that interests you the most: the `Trainer` API is great for fine-tuning or training your model without worrying about what's going on behind the scenes, while the training loop with `Accelerate` will let you customize any part you want more easily.

{:else}

To do this, you'll need to leverage everything you learned about training models with the Keras API in [Chapter 3](/course/chapter3), the 🤗 Datasets library in [Chapter 5](/course/chapter5), and the 🤗 Tokenizers library in [Chapter 6](/course/chapter6). We'll also upload our results to the Model Hub, like we did in [Chapter 4](/course/chapter4), so this is really the chapter where everything comes together!

Each section can be read independently.

{/if}


> [!TIP]
> If you read the sections in sequence, you will notice that they have quite a bit of code and prose in common. The repetition is intentional, to allow you to dip in (or come back later) to any task that interests you and find a complete working example.
